{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using gpu device 0: GeForce GTX 1070 (CNMeM is enabled with initial size: 75.0% of memory, cuDNN 5005)\n"
     ]
    }
   ],
   "source": [
    "import theano\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import MDBN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adding a layer with 559 input and 40 outputs\n",
      "Adding a layer with 19937 input and 400 outputs\n",
      "Adding a layer with 400 input and 40 outputs\n",
      "Adding a layer with 1683 input and 40 outputs\n",
      "Adding a layer with 120 input and 24 outputs\n",
      "Adding a layer with 24 input and 3 outputs\n"
     ]
    }
   ],
   "source": [
    "import AML\n",
    "me_DBN, ge_DBN, sm_DBN, dm_DBN, top_DBN = AML.load_network('Exp_2016-12-18_1435_run_0.npz','../MDBN_run')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "datafiles = AML.prepare_AML_TCGA_datafiles('../data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ME_output, _ = me_DBN.MLP_output_from_datafile(datafiles['ME'], datadir='../data')\n",
    "GE_output, _ = ge_DBN.MLP_output_from_datafile(datafiles['GE'], datadir='../data')\n",
    "SM_output, _ = sm_DBN.MLP_output_from_datafile(datafiles['SM'], datadir='../data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 119.5, 169.5, -0.5)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wI1Ylmk6m9kQqSzjLCBO1Tk/T8h2mYX0HK476TDsWWc4t0lCacgEAfv7nf76/5o791a9+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\neceX2ACEVnMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb7a3442b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "joint_layer = np.concatenate([ME_output, GE_output, SM_output],axis=1)\n",
    "plt.imshow(joint_layer, cmap='gray',interpolation='none')\n",
    "plt.axis('tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x7fb79f814450>,\n",
       "  <matplotlib.axis.XTick at 0x7fb79f814e50>,\n",
       "  <matplotlib.axis.XTick at 0x7fb79f769190>],\n",
       " <a list of 3 Text xticklabel objects>)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4xmERJtJH3grLSXetFh4RP5B0PUlWYhK4D7iq24G7DsGT9HJge0Q8I2l/4BZg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\nA8xB2sxsgDlIm5kNMAdpM7MB9v8BE58alJ/dcEkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb79f8b5910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_output = top_DBN.get_output(theano.shared(joint_layer,borrow=True))\n",
    "plt.imshow((top_output>0.8)*np.ones_like(top_output)-(top_output<0.2)*np.ones_like(top_output),interpolation='none',extent=[0,3,385,0])\n",
    "plt.colorbar()\n",
    "plt.axis('tight')\n",
    "plt.xticks(np.arange(0.5,3.5,1),('0','1','2'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x7fb79f714990>,\n",
       "  <matplotlib.axis.XTick at 0x7fb79f71f090>,\n",
       "  <matplotlib.axis.XTick at 0x7fb79f631910>],\n",
       " <a list of 3 Text xticklabel objects>)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5J61RkHQY8CngQ43aBp2EN/l2yNHb36+P88oaefhgp18AzV5Kb3T1JuF3Towe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\nzjkX0P8HdzugHVxXBvYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb79f750490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(top_output, interpolation='none',extent=[0,3,385,0])\n",
    "plt.axis('tight')\n",
    "plt.colorbar()\n",
    "plt.xticks(np.arange(0.5,3.5,1),('0','1','2'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([array([ 66.,   5.,   4.,   1.,   2.,   3.,   5.,   4.,   7.,  73.]),\n",
       "  array([ 93.,   5.,   4.,   4.,   1.,   5.,   2.,   3.,   3.,  50.]),\n",
       "  array([ 86.,   7.,   5.,   6.,   5.,   1.,   3.,   6.,   6.,  45.])],\n",
       " array([  2.86037452e-08,   9.99999661e-02,   1.99999904e-01,\n",
       "          2.99999841e-01,   3.99999779e-01,   4.99999716e-01,\n",
       "          5.99999654e-01,   6.99999591e-01,   7.99999529e-01,\n",
       "          8.99999466e-01,   9.99999404e-01]),\n",
       " <a list of 3 Lists of Patches objects>)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uiyPa/QvweeBNk657gp+LLcCtwMm918+adN0T7IuLgIsP9wPwI2DTpGsfQV+8EjgTuGWZ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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb79f750a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(top_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 86.,   7.,   5.,   6.,   5.,   1.,   3.,   6.,   6.,  45.]),\n",
       " array([  1.25142790e-07,   9.99985033e-02,   1.99996881e-01,\n",
       "          2.99995260e-01,   3.99993638e-01,   4.99992016e-01,\n",
       "          5.99990394e-01,   6.99988772e-01,   7.99987150e-01,\n",
       "          8.99985529e-01,   9.99983907e-01]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lwNOBVyS5cFSFSZKGt5k/lD4X+GxVnQBI8nfAHuDWURQmjd/0n2qVRm0zof59wBfXvP4S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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb79c056c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(top_output[:,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "operands could not be broadcast together with shapes (170,2) (170,3) ",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-26-9f2ab70cd261>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcode\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mtop_output\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0.5\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mones_like\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtop_output\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m: operands could not be broadcast together with shapes (170,2) (170,3) "
     ]
    }
   ],
   "source": [
    "code = (top_output[:,0:2] > 0.5) * np.ones_like(top_output[:,0:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from utils import find_unique_classes\n",
    "U = find_unique_classes(code)\n",
    "cl = U[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.,  5.,  4.,  6.,  6.,  0.,  3.,  4.,  1.,  4.,  5.,  4.,  5.,\n",
       "        4.,  6.,  4.,  4.,  2.,  0.,  1.,  1.,  5.,  0.,  0.,  1.,  1.,\n",
       "        1.,  1.,  1.,  5.,  4.,  2.,  2.,  5.,  1.,  4.,  0.,  1.,  7.,\n",
       "        1.,  6.,  0.,  4.,  3.,  7.,  7.,  4.,  4.,  1.,  1.,  2.,  4.,\n",
       "        1.,  1.,  7.,  6.,  4.,  1.,  4.,  2.,  1.,  6.,  6.,  4.,  2.,\n",
       "        0.,  4.,  6.,  1.,  6.,  4.,  6.,  1.,  1.,  7.,  4.,  4.,  4.,\n",
       "        2.,  7.,  7.,  0.,  5.,  6.,  4.,  2.,  7.,  3.,  1.,  4.,  4.,\n",
       "        6.,  1.,  5.,  4.,  0.,  6.,  2.,  0.,  1.,  0.,  3.,  6.,  7.,\n",
       "        2.,  6.,  1.,  4.,  1.,  3.,  7.,  0.,  7.,  2.,  0.,  7.,  4.,\n",
       "        6.,  4.,  1.,  4.,  4.,  0.,  1.,  6.,  1.,  4.,  4.,  4.,  4.,\n",
       "        7.,  4.,  1.,  4.,  4.,  4.,  5.,  6.,  4.,  0.,  6.,  0.,  2.,\n",
       "        1.,  2.,  4.,  2.,  1.,  4.,  1.,  4.,  6.,  2.,  5.,  4.,  2.,\n",
       "        4.,  4.,  2.,  2.,  0.,  2.,  6.,  0.,  4.,  2.,  0.,  7.,  6.,  2.])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 19.,  32.,  22.,   5.,  46.,  10.,  22.,  14.]),\n",
       " array([ 0.   ,  0.875,  1.75 ,  2.625,  3.5  ,  4.375,  5.25 ,  6.125,  7.   ]),\n",
       " <a list of 8 Patch objects>)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GjLgkNWbEJakxIy5JjRlxSWrMiEtSY0Zckhoz4pLU2P8DBI6iAXv971gAAAAASUVORK5C\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb79f445ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(cl,bins=8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check Survival curves for the different classes\n",
    "==============================================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import csv\n",
    "id=[]\n",
    "with open('../data/'+datafiles['ME']) as f:\n",
    "    my_csv = csv.reader(f,delimiter='\\t')\n",
    "    id = my_csv.next()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "stat={}\n",
    "with open('../data/AML/AML_clinical_data2.csv') as f:\n",
    "    reader = csv.reader(f, delimiter=',')\n",
    "    for row in reader:\n",
    "        patient_id=row[0]\n",
    "        stat[patient_id]=(row[4],row[7],row[6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The following case IDs were  not found in clinical data\n",
      "No data for TCGA-AB-2887\n",
      "No data for TCGA-AB-2891\n",
      "No data for TCGA-AB-2918\n",
      "No data for TCGA-AB-2921\n",
      "No data for TCGA-AB-2930\n",
      "No data for TCGA-AB-2940\n",
      "No data for TCGA-AB-2943\n",
      "No data for TCGA-AB-2946\n",
      "No data for TCGA-AB-2975\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "time_list = []\n",
    "event_list = []\n",
    "group_list = []\n",
    "print('The following case IDs were  not found in clinical data')\n",
    "for index, key in enumerate(id[1:]):\n",
    "    m = re.match('TCGA-\\w+-\\d+', key)\n",
    "    patient_id = m.group(0)\n",
    "    if patient_id in stat:\n",
    "        patient_stat = stat[patient_id]\n",
    "        add_group = True\n",
    "        try:\n",
    "            time_list.append(float(patient_stat[2]))\n",
    "            event_list.append(1)\n",
    "        except ValueError:\n",
    "            try:\n",
    "                time_list.append(float(patient_stat[1]))\n",
    "                event_list.append(0)\n",
    "            except ValueError:\n",
    "                print('No data for %s' % patient_id)\n",
    "                add_group = False\n",
    "        if add_group:\n",
    "            group_list.append(cl[index])\n",
    "    else:\n",
    "        print(patient_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb79c3ecc90>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sx8XAf4TbSW/nvcAKd/9e5FhaP9OD2priz7U6vVPC/wDbCN4Urwbm1vqN7iDb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\ngriISIIpiIuIJJiCuIhIgimIi4gk2P8HcgdpOWgfII4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb79c3dbd50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from lifelines import KaplanMeierFitter\n",
    "kmf = KaplanMeierFitter()\n",
    "kmf.fit(time_list,event_observed=event_list)\n",
    "kmf.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Ew6IK4mY2xcy2mNlWM7ulgzoPmNk2M9tgZhWJbaaIiIQTMYibWQ/gIWAycAYw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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb79c3c3f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "T=np.array(time_list)\n",
    "E=np.array(event_list)\n",
    "ix = (np.array(group_list) == 0)\n",
    "kmf.fit(T[ix], E[ix], label='group 0')\n",
    "ax=kmf.plot()\n",
    "for i in range(1,4):\n",
    "    ix=(np.array(group_list)==i)\n",
    "    kmf.fit(T[ix], E[ix], label='group %d' % i)\n",
    "    kmf.plot(ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Z64+IBEMy98TPALY653YAmNkK4AKgI4g75172tH8ZGOVnJ7Ohu6o/fvFWD0oU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\nAkxBXEQkwBTERUQCTEFcRCTA/j8RS4pMZKrYlAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb79c1d3d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "T=np.array(time_list)\n",
    "E=np.array(event_list)\n",
    "ix = (np.array(group_list) == 4)\n",
    "kmf.fit(T[ix], E[ix], label='group 4')\n",
    "ax=kmf.plot()\n",
    "for i in range(5,8):\n",
    "    ix=(np.array(group_list)==i)\n",
    "    kmf.fit(T[ix], E[ix], label='group %d' % i)\n",
    "    kmf.plot(ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
